SEOUL - South Korea introduced on Thursday what it says is the world's first comprehensive set of laws regulating artificial intelligence, aiming to strengthen trust and safety in the sector, but startups fretted that compliance could hold them back. Seoul is hoping that the new AI Basic Act will position the country as a leader in the field. It has taken effect in South Korea sooner than a comparable effort in Europe, where the EU AI Act is being applied in phases through 2027. Global divisions remain over how to regulate AI, with the U.S. favoring a more light-touch approach to avoid stifling innovation. China has introduced some rules and proposed creating a body to coordinate global regulation. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Javed is a senior editor at TIME, based in the London bureau. Javed is a senior editor at TIME, based in the London bureau. Leaders from across the tech sector, academia, and beyond gathered to explore how to implement responsible AI and ensure safeguarding while fostering innovation, at a roundtable convened by TIME in Davos, Switzerland, on Jan 21. In a wide-ranging conversation, participants in the roundtable, hosted by TIME CEO Jess Sibley, discussed topics including the impact of AI on children's development and safety, how to regulate the technology, and how to better train models to ensure they don't harm humans. Discussing the safety of children, Jonathan Haidt, professor of ethical leadership at NYU Stern and author of said that parents shouldn't focus on restricting their child's exposure entirely but on the habits they form.

OpenAI could be valued at $1tn if it launches an initial public offering, Reuters said. OpenAI could be valued at $1tn if it launches an initial public offering, Reuters said. The year of the'hectocorn': the $100bn tech companies that could float in 2026 Y ou've probably heard of "unicorns" - technology startups valued at more than $1bn - but 2026 is shaping up to be the year of the " hectocorn ", with several US and European companies potentially floating on stock markets at valuations over $100bn (£75bn). OpenAI, Anthropic, SpaceX and Stripe are among the big names said to be considering an initial public offering (IPO) this year. The success of their flotations - whether the shares maintain their value, rise or fall - could shape concerns about the AI race and whether the resulting market mania is a bubble .

An Australian tourist describes seeing one of the landslides behind him and running from it.

Ashton Kutcher: Hollywood isn't to blame for pushing unrealistic beauty standards US actor Ashton Kutcher has said he believes Hollywood is not pushing unreasonably high beauty standards, adding that wider society is to blame for the increasing desire to look perfect. The 47-year-old is currently starring in science fiction show The Beauty, which sees a drug become available that can transform a person into the most attractive version of themselves. Speaking to BBC News, Kutcher said he does not believe the film and TV industry is imparting the need for aesthetic homogeny. Entertainment is a reflection of society, he said. Across the different characters and actors in shows, some are traditionally handsome but others are just really interesting, he said.

The Oscar nominations will be announced later, with Leonardo DiCaprio's politically-charged thriller One Battle After Another expected to lead the field. Marty Supreme, Frankenstein, Sentimental Value, Bugonia and The Secret Agent are also expected to perform strongly when the shortlists are announced from 13:30 GMT. It's a weaker year for UK talent - Wunmi Mosaku from vampire horror Sinners is one of the few British stars with a chance of securing an acting nomination. But Irish actors Jessie Buckley and Paul Mescal are expected to be recognised for their roles in the screen adaptation of Maggie O'Farrell's novel Hamnet. US comedian Conan O'Brien will return to host this year's Academy Awards ceremony, which takes place on 15 March.

We study the problem of estimating causal effects under hidden confounding in the following unpaired data setting: we observe some covariates $X$ and an outcome $Y$ under different experimental conditions (environments) but do not observe them jointly; we either observe $X$ or $Y$. Under appropriate regularity conditions, the problem can be cast as an instrumental variable (IV) regression with the environment acting as a (possibly high-dimensional) instrument. When there are many environments but only a few observations per environment, standard two-sample IV estimators fail to be consistent. We propose a GMM-type estimator based on cross-fold sample splitting of the instrument-covariate sample and prove that it is consistent as the number of environments grows but the sample size per environment remains constant. We further extend the method to sparse causal effects via $\ell_1$-regularized estimation and post-selection refitting.

Principal component analysis (PCA) is a tool to capture factors that explain variation in data. Across domains, data are now collected across multiple contexts (for example, individuals with different diseases, cells of different types, or words across texts). While the factors explaining variation in data are undoubtedly shared across subsets of contexts, no tools currently exist to systematically recover such factors. We develop multi-context principal component analysis (MCPCA), a theoretical and algorithmic framework that decomposes data into factors shared across subsets of contexts. Applied to gene expression, MCPCA reveals axes of variation shared across subsets of cancer types and an axis whose variability in tumor cells, but not mean, is associated with lung cancer progression. Applied to contextualized word embeddings from language models, MCPCA maps stages of a debate on human nature, revealing a discussion between science and fiction over decades. These axes are not found by combining data across contexts or by restricting to individual contexts. MCPCA is a principled generalization of PCA to address the challenge of understanding factors underlying data across contexts.

Robust machine learning for regulatory genomics is studied under biologically and technically induced distribution shifts. Deep convolutional and attention based models achieve strong in distribution performance on DNA regulatory sequence prediction tasks but are usually evaluated under i.i.d. assumptions, even though real applications involve cell type specific programs, evolutionary turnover, assay protocol changes, and sequencing artifacts. We introduce a robustness framework that combines a mechanistic simulation benchmark with real data analysis on a massively parallel reporter assay (MPRA) dataset to quantify performance degradation, calibration failures, and uncertainty based reliability. In simulation, motif driven regulatory outputs are generated with cell type specific programs, PWM perturbations, GC bias, depth variation, batch effects, and heteroscedastic noise, and CNN, BiLSTM, and transformer models are evaluated. Models remain accurate and reasonably calibrated under mild GC content shifts but show higher error, severe variance miscalibration, and coverage collapse under motif effect rewiring and noise dominated regimes, revealing robustness gaps invisible to standard i.i.d. evaluation. Adding simple biological structural priors motif derived features in simulation and global GC content in MPRA improves in distribution error and yields consistent robustness gains under biologically meaningful genomic shifts, while providing only limited protection against strong assay noise. Uncertainty-aware selective prediction offers an additional safety layer that risk coverage analyses on simulated and MPRA data show that filtering low confidence inputs recovers low risk subsets, including under GC-based out-of-distribution conditions, although reliability gains diminish when noise dominates.

The rapid progress of diffusion models highlights the growing need for detecting generated images. Previous research demonstrates that incorporating diffusion-based measurements, such as reconstruction error, can enhance the gener-alizability of detectors. However, ignoring the differing impacts of aleatoric and epistemic uncertainty on reconstruction error can undermine detection performance. Aleatoric uncertainty, arising from inherent data noise, creates ambiguity that impedes accurate detection of generated images. As it reflects random variations within the data (e.g., noise in natural textures), it does not help distinguish generated images. In contrast, epistemic uncertainty, which represents the model's lack of knowledge about unfamiliar patterns, supports detection. In this paper, we propose a novel framework, Diffusion Epistemic Uncertainty with Asymmetric Learning (DEUA), for detecting diffusion-generated images. W e introduce Diffusion Epistemic Uncertainty (DEU) estimation via the Laplace approximation to assess the proximity of data to the manifold of diffusion-generated samples. Additionally, an asymmetric loss function is introduced to train a balanced classifier with larger margins, further enhancing generalizability.